Arguments Whose Strength Depends on Continuous Variation

Both the traditional Aristotelian and modern symbolic approaches to logic have seen logic in terms of discrete symbol processing. Yet there are several kinds of argument whose validity depends on some topological notion of continuous variation, which is not well captured by discrete symbols. Examples include extrapolation and slippery slope arguments, sorites, fuzzy logic, and those involving closeness of possible worlds. It is argued that the natural first attempts to analyze these notions and explain their relation to reasoning fail, so that ignorance of their nature is profound

Learning object behaviour models

The human visual system is capable of interpreting a remarkable variety of often subtle, learnt, characteristic behaviours. For instance we can determine the gender of a distant walking figure from their gait, interpret a facial expression as that of surprise, or identify suspicious behaviour in the movements of an individual within a car-park. Machine vision systems wishing to exploit such behavioural knowledge have been limited by the inaccuracies inherent in hand-crafted models and the absence of a unified framework for the perception of powerful behaviour models. The research described in this thesis attempts to address these limitations, using a statistical modelling approach to provide a framework in which detailed behavioural knowledge is acquired from the observation of long image sequences. The core of the behaviour modelling framework is an optimised sample-set representation of the probability density in a behaviour space defined by a novel temporal pattern formation strategy. This representation of behaviour is both concise and accurate and facilitates the recognition of actions or events and the assessment of behaviour typicality. The inclusion of generative capabilities is achieved via the addition of a learnt stochastic process model, thus facilitating the generation of predictions and realistic sample behaviours. Experimental results demonstrate the acquisition of behaviour models and suggest a variety of possible applications, including automated visual surveillance, object tracking, gesture recognition, and the generation of realistic object behaviours within animations, virtual worlds, and computer generated film sequences. The utility of the behaviour modelling framework is further extended through the modelling of object interaction. Two separate approaches are presented, and a technique is developed which, using learnt models of joint behaviour together with a stochastic tracking algorithm, can be used to equip a virtual object with the ability to interact in a natural way. Experimental results demonstrate the simulation of a plausible virtual partner during interaction between a user and the machine

Theoretical study of metal borides stability

We have recently identified metal-sandwich (MS) crystal structures and shown with ab initio calculations that the MS lithium monoboride phases are favored over the known stoichiometric ones under hydrostatic pressure [Phys. Rev. B 73, 180501(R) (2006)]. According to previous studies synthesized lithium monoboride tends to be boron-deficient, however the mechanism leading to this phenomenon is not fully understood. We propose a simple model that explains the experimentally observed off-stoichiometry and show that compared to such boron-deficient phases the MS-LiB compounds still have lower formation enthalpy under high pressures. We also investigate stability of MS phases for a large class of metal borides. Our ab initio results suggest that MS noble metal borides are less unstable than the corresponding AlB$_2$-type phases but not stable enough to form under equilibrium conditions.Comment: 14 pages, 15 figure

Multidimensional Supernova Simulations with Approximative Neutrino Transport I. Neutron Star Kicks and the Anisotropy of Neutrino-Driven Explosions in Two Spatial Dimensions

By means of two-dimensional (2D) simulations we study hydrodynamic instabilities during the first seconds of neutrino-driven supernova explosions, using a PPM hydrodynamics code, supplemented with a gray, non-equilibrium approximation of radial neutrino transport. We consider three 15 solar mass progenitors with different structures and one rotating model, in which we replace the dense core of the newly formed neutron star (NS) by a contracting inner grid boundary, and trigger neutrino-driven explosions by systematically varying the neutrino fluxes emitted at this boundary. Confirming more idealized studies as well as supernova simulations with spectral transport, we find that random seed perturbations can grow by hydrodynamic instabilities to a globally asymmetric mass distribution, leading to a dominance of dipole (l=1) and quadrupole (l=2) modes in the explosion ejecta. Anisotropic gravitational and hydrodynamic forces are found to accelerate the NS on a timescale of 2-3 seconds. Since the explosion anisotropies develop chaotically, the magnitude of the corresponding kick varies stochastically in response to small differences in the fluid flow. Our more than 70 models separate into two groups, one with high and the other with low NS velocities and accelerations after 1s of post-bounce evolution, depending on whether the l=1 mode is dominant in the ejecta or not. This leads to a bimodality of the distribution when the NS velocities are extrapolated to their terminal values. The fast group has an average velocity of about 500 km/s and peak values in excess of 1000 km/s. Establishing a link to the measured distribution of pulsar velocities, however, requires a much larger set of calculations and ultimately 3D modeling. (abridged)Comment: 40 pages, 28 figures; significantly shortened and revised version according to referee's comments; accepted by Astronomy & Astrophysic

Three-dimensional simulation of massive star formation in the disk accretion scenario

The most massive stars can form via standard disk accretion - despite of the radiation pressure generated - due to the fact that the massive accretion disk yields a strong anisotropy in the radiation field, releasing most of the radiation pressure perpendicular to the disk accretion flow. Here, we analyze the self-gravity of the forming circumstellar disk as the potential major driver of the angular momentum transport in such massive disks responsible for the high accretion rates needed for the formation of massive stars. For this purpose, we perform self-gravity radiation hydrodynamics simulations of the collapse of massive pre-stellar cores. The formation and evolution of the resulting circumstellar disk is investigated in 1.) axially symmetric simulations using an alpha-shear-viscosity prescription and 2.) a three-dimensional simulation, in which the angular momentum transport is provided self-consistently by developing gravitational torques in the self-gravitating accretion disk. The simulation series of different strength of the alpha-viscosity shows that the accretion history of the forming star is mostly independent of the alpha-viscosity-parameter. The accretion history of the three-dimensional run driven by self-gravity is more time-dependent than the viscous disk evolution in axial symmetry. The mean accretion rate, i.e. the stellar mass growth, is nearly identical to the alpha-viscosity models. We conclude that the development of gravitational torques in self-gravitating disks around forming massive stars provides a self-consistent mechanism to efficiently transport the angular momentum to outer disk radii. Also the formation of the most massive stars can therefore be understood in the standard accretion disk scenario.Comment: accepted for publication at Ap

The Skyrme Interaction in finite nuclei and nuclear matter

Self-consistent mean-field models are a powerful tool in the investigation of nuclear structure and low-energy dynamics. They are based on effective energy-density functionals, often formulated in terms of effective density-dependent nucleon-nucleon interactions. The free parameters of the functional are adjusted to empirical data. A proper choice of these parameters requires a comprehensive set of constraints covering experimental data on finite nuclei, concerning static as well as dynamical properties, empirical characteristics of nuclear matter, and observational information on nucleosynthesis, neutron stars and supernovae. This work aims at a comprehensive survey of the performance of one of the most successful non-relativistic self-consistent method, the Skyrme-Hartree-Fock model (SHF), with respect to these constraints. A full description of the Skyrme functional is given and its relation to other effective interactions is discussed. The validity of the application of SHF far from stability and in dense environments beyond the nuclear saturation density is critically assessed. The use of SHF in models extended beyond the mean field approximation by including some correlations is discussed. Finally, future prospects for further development of SHF towards a more consistent application of the existing and promisingly newly developing constraints are outlined.Comment: 71 pages, 22 figures. Accepted for publication in Prog.Part.Nucl.Phy

Large-Scale Light Field Capture and Reconstruction

This thesis discusses approaches and techniques to convert Sparsely-Sampled Light Fields (SSLFs) into Densely-Sampled Light Fields (DSLFs), which can be used for visualization on 3DTV and Virtual Reality (VR) devices. Exemplarily, a movable 1D large-scale light field acquisition system for capturing SSLFs in real-world environments is evaluated. This system consists of 24 sparsely placed RGB cameras and two Kinect V2 sensors. The real-world SSLF data captured with this setup can be leveraged to reconstruct real-world DSLFs. To this end, three challenging problems require to be solved for this system: (i) how to estimate the rigid transformation from the coordinate system of a Kinect V2 to the coordinate system of an RGB camera; (ii) how to register the two Kinect V2 sensors with a large displacement; (iii) how to reconstruct a DSLF from a SSLF with moderate and large disparity ranges. To overcome these three challenges, we propose: (i) a novel self-calibration method, which takes advantage of the geometric constraints from the scene and the cameras, for estimating the rigid transformations from the camera coordinate frame of one Kinect V2 to the camera coordinate frames of 12-nearest RGB cameras; (ii) a novel coarse-to-fine approach for recovering the rigid transformation from the coordinate system of one Kinect to the coordinate system of the other by means of local color and geometry information; (iii) several novel algorithms that can be categorized into two groups for reconstructing a DSLF from an input SSLF, including novel view synthesis methods, which are inspired by the state-of-the-art video frame interpolation algorithms, and Epipolar-Plane Image (EPI) inpainting methods, which are inspired by the Shearlet Transform (ST)-based DSLF reconstruction approaches